{"product_id":"9781461453680","title":"Springer Series in Statistics","description":"\u003ch1\u003eSpringer Series in Statistics\u003c\/h1\u003e \u003ch2\u003eGu, Chong\u003c\/h2\u003e \u003cp\u003e\u003c\/p\u003e\u003cp\u003eNonparametric function estimation with stochastic data, otherwise\u003c\/p\u003e\u003cp\u003eknown as smoothing, has been studied by several generations of\u003c\/p\u003e\u003cp\u003estatisticians. Assisted by the ample computing power in today's\u003c\/p\u003e\u003cp\u003eservers, desktops, and laptops, smoothing methods have been finding\u003c\/p\u003e\u003cp\u003etheir ways into everyday data analysis by practitioners. While scores\u003c\/p\u003e\u003cp\u003eof methods have proved successful for univariate smoothing, ones\u003c\/p\u003e\u003cp\u003epractical in multivariate settings number far less. Smoothing spline\u003c\/p\u003e\u003cp\u003eANOVA models are a versatile family of smoothing methods derived\u003c\/p\u003e\u003cp\u003ethrough roughness penalties, that are suitable for both univariate and\u003c\/p\u003e\u003cp\u003emultivariate problems.\u003c\/p\u003e\u003cp\u003eIn this book, the author presents a treatise on penalty smoothing\u003c\/p\u003e\u003cp\u003eunder a unified framework. Methods are developed for (i) regression\u003c\/p\u003e\u003cp\u003ewith Gaussian and non-Gaussian responses as well as with censored lifetime data; (ii) density and conditional density estimation under a\u003c\/p\u003e\u003cp\u003evariety of sampling schemes; and (iii) hazard rate estimation with\u003c\/p\u003e\u003cp\u003ecensored life time data and covariates. The unifying themes are the\u003c\/p\u003e\u003cp\u003egeneral penalized likelihood method and the construction of\u003c\/p\u003e\u003cp\u003emultivariate models with built-in ANOVA decompositions. Extensive\u003c\/p\u003e\u003cp\u003ediscussions are devoted to model construction, smoothing parameter\u003c\/p\u003e\u003cp\u003eselection, computation, and asymptotic convergence.\u003c\/p\u003e\u003cp\u003eMost of the computational and data analytical tools discussed in the\u003c\/p\u003e\u003cp\u003ebook are implemented in R, an open-source platform for statistical\u003c\/p\u003e\u003cp\u003ecomputing and graphics. Suites of functions are embodied in the R\u003c\/p\u003e\u003cp\u003epackage gss, and are illustrated throughout the book using simulated\u003c\/p\u003e\u003cp\u003eand real data examples.\u003c\/p\u003e\u003cp\u003eThis monograph will be useful as a reference work for researchers in\u003c\/p\u003e\u003cp\u003etheoretical and applied statistics as well as for those in other\u003c\/p\u003e\u003cp\u003erelated disciplines. It can also be used as a text for graduate level\u003c\/p\u003e\u003cp\u003ecourses on the subject. Most of the materials are accessibleto a\u003c\/p\u003e\u003cp\u003esecond year graduate student with a good training in calculus and\u003c\/p\u003e\u003cp\u003elinear algebra and working knowledge in basic statistical inferences\u003c\/p\u003e\u003cp\u003esuch as linear models and maximum likelihood estimates.\u003c\/p\u003e \u003ch3\u003eDetails\u003c\/h3\u003e \u003cp\u003ePublished by: Springer\u003c\/p\u003e \u003cp\u003ePublication Date: 2013-01-25\u003c\/p\u003e \u003cp\u003eFormat: Hardcover\u003c\/p\u003e \u003cp\u003eISBN-13: 9781461453680\u003c\/p\u003e \u003cp\u003eDOI: 10.1007\/978-1-4614-5369-7\u003c\/p\u003e \u003cp\u003eDimensions: 235cm x155cm\u003c\/p\u003e \u003cp\u003ePages: 433\u003c\/p\u003e ","brand":"Springer New York","offers":[{"title":"Default Title","offer_id":45937768530060,"sku":"9781461453680","price":161.1,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9781461453680.jpg?v=1775711065","url":"https:\/\/lateknightbooks.com\/products\/9781461453680","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}